criemen
criemen on Hacker News
> Remember infrared file sharing? Of course you don’t.
Hey, I played with that when I was approximately 8 years old! Also: wireless printing via infrared, instead of those huge parallel printer cables.
> A large number of companies offer 0% consumer exposure: Nvidia
I thought they're still selling gaming GPUs? What did I miss?
Fascinating read, thanks! The counterpoint to "slaughtering the golden goose" is: If you're all in with AI, this is the rational thing to do, no?
If AI takes over, search becomes irrelevant, so better extract as much value from search as possible while it's decaying and not dead yet.
The thing that I've not fully groked here (might be relevant) is that OAI is generating "paper" proofs first, and only then formalizes them in Lean as a separate step.
They've as far as I'm aware not said anywhere how many write ups failed formalization due to a bug in the proof. If we assume that number is low, models would be quite good at one-shotting math proofs. Then it depends if you can replicate the math RL training for chemistry/biology/medicine. Is there enough data out there for that? No idea.
Wow. Complexity class separations are really really rare.
Can an expert in sat solving comment if there's any immediate applications of the method from the paper? Or is SAT solving in general pretty far away from worst-case times, and this is a theoretical algorithm which would be hard to apply in practice.
I'm curious, why would this not turn into an unlimited electricity flatrate? In most parts of the world, consumer electricity is charged by kWh, not as flat payment.
As long as tokens are as energy intense to compute as they are right now, I don't see a flatrate being a viable model.
Is math or theoretical Computer Science not "real science"? Whatever one may think about the process, the results are real and are coming from LLMs.
SpaceX Tesla crossover. Not exactly what was on my bingo sheet today.
It's really funny how the server that Jev pushes from the footbridge onto the tracks in one of the later examples falls on a person, yet all 5 are saved :D
Clearly the model coming up with the graphics didn't fully understand the task.
I'd add missing MFA as weakness number three, at minimum.
Problem number 4 is that the "password" was leaked, and the company (Pays ApS) didn't figure that out.
Problem number 5 - the "password" belonged to a _former_ employee. How was that account not disabled?
Problem number 6 - how can a company with two employees get access to this register in the first place? Don't they need to show compliance with some security standard that would be not possible to deliver for such a small company?
If you start thinking more about this, more and more problems pop up.
On the other hand, Francis Halzen reportedly never went down to the South Pole to the facility at all, as probably the only person working on IceCube :D
Posted by
criemen on Tuesday October 06, 2026 @05:17PM
from the youtube.com dept.
Let's see:
1. paper
2. cardboard
3. organic
4. metal
5. residual waste
6. batteries
7. hazardous waste
8. garden waste
9. plastic
10. indoor wood
11. large items (i.e. furniture)
12. glass
13. electronics
All of which is picked up from my apartment buildings backyard.
> all models have search capacities these days
one would hope that they disable websearch and internet access (maybe all tools?) when doing generalization testing?
> In addition to things like battery-backed smoke detectors in every bedroom, etc, which need periodic 9V replacements on a schedule.
Just as aside, lithium-battery powered smoke detectors are a godsend. The battery lifespan lasts the recommended life span of the detector, i.e. around 10 years.
No battery replacements needed, although you still ought to test them once in a while.
Just 5? Lucky you, here (depending on how you count) we're doing 13 fractions.
It doesn't even make sense.
> On Apple's unified memory that transfer compiles to almost nothing. It is still written down, because data locality should be provable by reading the source rather than by profiling the binary.
How does profiling the binary play into this? Elsewhere the hypothesis is "it crashes", whereas profiling is about performance.
> Most languages treat the accelerator as infrastructure: you write math, and a large opaque runtime decides how to ship it. Vx treats it as semantics.
What does that mean? Vx doesn't treat the accelerator as infrastructure? Vx doesn't have a large opaque runtime? "You write math, Vx treats it as semantics". I don't understand that.
If launch costs are assumed to be $0/kg, how does that solve the waste heat problem? Add more mass? What about the maintenance of failing hardware?
I'm genuinely curious, I thought basically heat radiation was a hard physical boundary, regardless of launch cost.
That's a bit out of context, no?
The preceding sentence is
> Even DeepMind, with its exceptional budget, couldn’t build a machine that reliably beat the best human players.
so the contrast here is the budget available, not the quality of the talent, if we accept the premise that DeepMind and the universities have approximately similar level of talent.
They also have a distinct style/naming convention, see for example `a_list_entry` as type name.
Wikipedia claims they have 6 employees, so they didn't exactly enjoy exponential growth over the years ;)
My kettle (Fellow EKG) has an annoying display bug. With a simple prompt, Astra went and downloaded the firmware, disassembled it, interpreted it and confirmed the bug. It also told me that there's a firmware signature check, so I have unfortunately no way of flashing a fixed firmware myself.
I'm not sure I'd be adventurous enough to do that anyways, as I don't know enough about ESP32 microcontrollers to verify what it's doing vs. the chance of bricking my device.
Yet,
> but openai went kamikaze
I don't quite understand your point here. OpenAI has a consistent history of releasing cheap/small models - first nano/mini, then luna/terra. Of course, those are now more capable than half a year ago, but I don't see a behavior change from OpenAI here.
The latest Haiku release is almost a year old. Clearly they don't care about the small-but-capable part of the market at all.
> to what end?
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
My apologies, and it's too late to edit the parent now.
To be fair, though, I mainly ran this experiment to distinguish models, not to see if they were better than humans or not, so for that purpose it didn't really matter if the basic conditions were the same or not.